We evaluate the proposed method on our private dataset and DDSM dataset. The private dataset was retrospectively collected from Hanoi Medical University Hospital from 2018 to 2020. Each image was assigned to a team of three radiologists specializing in breast imaging for multilabel annotation (BI-RADS 1\(\div \) 5 and breast density A\(\div \) D). In total, the dataset includes 36,138 screening mammogram images from 9,911 studies. There are 8,509 four-view studies, which consist of one image per view (L-CC, L-MLO, R-CC, R-MLO). These exams were divided into three groups by the multilabel stratification method [1]}: training set (5,792 studies), validation set (1,266 studies), and test set (1,271 studies). Descriptions of three sets on the private dataset are provided in Table REF . For the DDSM dataset, the number of exams that are multilabel-stratified in training, validation, test set is 1,822; 391; and 391, respectively. Each study involves two images of each breast, along with pathology label and density information. There are four types of labels, normal - benign - cancer - benign with callback - benign without callback. The DDSM training set contains 486 normal, 704 benign, and 632 cancer cases. The figures of normal/benign/cancer studies in the validation set and test set are 105/147/139 and 104/145/142, respectively. For both datasets, each breast's pathology/BI-RADS label is the maximum label of its CC and MLO images, while the density of these two view images certainly has the same information.
